The present invention generally relates to a process control method and a process control apparatus. In particular, the present invention is directed to a process control method and a process control apparatus using fuzzy inference or fuzzy reasoning.
Conventionally, a PID control has been widely utilized as a process control. In accordance with this PID control, each of a controlled error or deviation between a set-point value, an integral value of this deviation, and a differential value of this deviation is summed with a control parameter in a weighting manner, and the summed value is used as a manipulating variable. The PID control has such an advantage that when a controlled object is approximated, or simulated to dead time and a first order delay, an experimental setting basis (for instance, Ziegler Nichols' critical sensitivity method) of the control parameter is given, and a relatively simple adjustment is achieved.
Very recently, to further improve control performance, such novel control method as a modern control theory and a fuzzy control have been practically utilized. In particular, this fuzzy control is one of the most practical control methods which have been utilized since 1980.
In the conventional fuzzy control applications to the fuzzy process control method, both of a controlled error and a variation contained in the controlled errors are employed as input values (see Japanese publication "FUZZY CONTROL, SYSTEM & CONTROL" written by Yamazaki and Suzano, Vol. 28, No. 7, 1984, pages 442 to 446). In case that there are 5 prepositions (positive/large, positive/medium, zero, negative/medium, negative/large) in the fuzzy inference, a total number of combinations made by fuzzy rules in which deviation and a change in this deviation are employed as input values, becomes 1 to 25. In this case, the number of fuzzy set, namely the quantity of membership function is such that the number of conditional parts is 10, and the number of conclusion parts is 5.
In the conventional fuzzy controls, since two input values of the controlled error and the variation thereof are entered, the total number of fuzzy rules with respect to two input values becomes 25, whereas that for one input values becomes only 5. As a result, a cumbersome system construction is required with respect to adjustments of membership functions, and also a lengthy calculation time is required for the conventional fuzzy control. Moreover, since the variation contained in the controlled errors is utilized as the input value, an adverse influence caused by noise is given to the conventional fuzzy control. When a controlled object contains dead time, control performance of the conventional fuzzy control is lowered.